Real-time decentralized neural backstepping controller for a robot manipulator

Author

Garcia-Hernandez, R. ; Sanchez, E.N. ; Nez, V. Santiba ; Llama, M.A. ; Bayro-Corrochano, E.

Author_Institution

Univ. Autonoma del Carmen, Campeche, Mexico

fYear

2009

Firstpage

701

Lastpage

706

Abstract

This paper deals with adaptive trajectory tracking for discrete-time MIMO nonlinear systems. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The HONN learning is performed online by an Extended Kalman Filter (EKF) algorithm. The proposed scheme is implemented in real-time to control a two DOF robot manipulator.

Keywords

Kalman filters; MIMO systems; decentralised control; discrete time systems; feedback; learning (artificial intelligence); manipulators; neurocontrollers; nonlinear control systems; nonlinear filters; position control; tracking; adaptive trajectory tracking; backstepping technique; block strict feedback form; decentralized control law; discrete-time MIMO nonlinear systems; extended Kalman filter algorithm; high order neural network; online learning; real-time decentralized neural backstepping controller; two DOF robot manipulator; Adaptive control; Backstepping; Control systems; Distributed control; Manipulator dynamics; Mobile robots; Neural networks; Nonlinear control systems; Programmable control; Robot control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE

Conference_Location

St. Petersburg

Print_ISBN

978-1-4244-4601-8

Electronic_ISBN

978-1-4244-4602-5

Type

conf

DOI

10.1109/CCA.2009.5280998

Filename

5280998